{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a392bf3-8861-4cf4-84cf-74953ab6c854",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8375f5f6-f8c1-4eca-8236-c4251f37350f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_location = '../datasets/'\n",
    "sf_load_df_raw = pd.read_csv(data_location +  'energy/SF_hospital_load.csv')\n",
    "sf_pv_df_raw = pd.read_csv(data_location +  'energy/SF_PV.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc98ae8-14de-4cd9-aff5-f695b295a8ce",
   "metadata": {},
   "source": [
    "## SF hospital load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cdf8ca77-2ea7-4b81-9536-7e6ec7f8bb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sf_load_df_raw)/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "483f7fd0-c362-4487-9062-cbd3b1914ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Electricity:Facility [kW](Hourly)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1012.454652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>198.329809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>715.644051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>820.487963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>931.869578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1242.089126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1388.981796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Electricity:Facility [kW](Hourly)\n",
       "count                        8760.000000\n",
       "mean                         1012.454652\n",
       "std                           198.329809\n",
       "min                           715.644051\n",
       "25%                           820.487963\n",
       "50%                           931.869578\n",
       "75%                          1242.089126\n",
       "max                          1388.981796"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_load_df_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5054d48a-d783-4671-b00f-7cfd1b370a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf_load_df_raw[-7*24*2:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "735a84d8-b561-48e6-b2b7-aa97bd01f29e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Electricity:Facility [kW](Hourly)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>778.007969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>776.241750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>779.357338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Electricity:Facility [kW](Hourly)\n",
       "0                         778.007969\n",
       "1                         776.241750\n",
       "2                         779.357338"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_load_df_raw.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2337a86-9105-46e3-9f12-8b13f739c1e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>778.007969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>776.241750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>779.357338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds           y\n",
       "0 2015-01-01 01:00:00  778.007969\n",
       "1 2015-01-01 02:00:00  776.241750\n",
       "2 2015-01-01 03:00:00  779.357338"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_load_df = pd.DataFrame()\n",
    "sf_load_df['ds'] = pd.date_range('1/1/2015 1:00:00', freq='H', periods=(8760)) \n",
    "sf_load_df['y'] = sf_load_df_raw.iloc[:,0].values\n",
    "\n",
    "sf_load_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9c35f5a-500e-438e-8232-d2273327499f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ds'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf_load_df.plot(x='ds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "937a3a76-7ba9-4f61-9bc7-8969da677a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sf_load_df.to_csv(data_location + 'energy/SF_hospital_load.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7bbff703-cca9-4176-a1ed-d56704055cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1012.454652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>198.329809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>715.644051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>820.487963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>931.869578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1242.089126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1388.981796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 y\n",
       "count  8760.000000\n",
       "mean   1012.454652\n",
       "std     198.329809\n",
       "min     715.644051\n",
       "25%     820.487963\n",
       "50%     931.869578\n",
       "75%    1242.089126\n",
       "max    1388.981796"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_load_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96861763-08f9-4c3a-b2d8-df0b01581c0c",
   "metadata": {},
   "source": [
    "## SF PV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "97bd4606-f672-4506-b83d-f9610b7d9d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds  y\n",
       "0 2015-01-01 01:00:00  0\n",
       "1 2015-01-01 02:00:00  0\n",
       "2 2015-01-01 03:00:00  0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_pv_df = pd.DataFrame()\n",
    "sf_pv_df['ds'] = pd.date_range('1/1/2015 1:00:00', freq='H', periods=(8760)) \n",
    "sf_pv_df['y'] = sf_pv_df_raw.iloc[:,0].values\n",
    "\n",
    "sf_pv_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "801c961f-6552-415f-a99f-c61a4862e773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GH illum (lx)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>208.199772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>295.637947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1069.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GH illum (lx)\n",
       "count    8760.000000\n",
       "mean      208.199772\n",
       "std       295.637947\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         6.000000\n",
       "75%       374.000000\n",
       "max      1069.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_pv_df_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7155c9f-d267-4523-992d-1b91969174e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>208.199772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>295.637947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1069.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 y\n",
       "count  8760.000000\n",
       "mean    208.199772\n",
       "std     295.637947\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       6.000000\n",
       "75%     374.000000\n",
       "max    1069.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_pv_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77ffec54-10ce-4206-895b-1b13d4e2e682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf_pv_df_raw.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0e56b0f-16f8-4925-a746-07d43b685bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf_pv_df_raw.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "596b92dc-f73a-42e9-9ff2-e0aec348c213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GH illum (lx)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GH illum (lx)\n",
       "0              0\n",
       "1              0\n",
       "2              0\n",
       "3              0\n",
       "4              0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_pv_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c61d5bbc-a581-4492-ab10-dc3fe756953a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 05:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds  y\n",
       "0 2015-01-01 01:00:00  0\n",
       "1 2015-01-01 02:00:00  0\n",
       "2 2015-01-01 03:00:00  0\n",
       "3 2015-01-01 04:00:00  0\n",
       "4 2015-01-01 05:00:00  0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_pv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aade60e5-76a5-439c-99eb-d4ad01505693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ds'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf_pv_df.plot(x='ds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e028cd9e-1b2e-4d79-8e17-ff20518b815d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sf_pv_df.to_csv(data_location + 'energy/SF_PV.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a2c9bb0-265d-45e2-abf2-3ea0a257b2f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "np-dev",
   "language": "python",
   "name": "np-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
